{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bda49867",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mjm/opt/anaconda3/envs/ntk_ps/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torchvision import transforms\n",
    "from functorch import make_functional, vmap, vjp, jvp, jacrev\n",
    "from torchvision import datasets\n",
    "import torch.utils.data as loader\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import linalg as LA\n",
    "import torch.nn as nn\n",
    "from numpy.random import default_rng\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "rng = default_rng()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf2ecf8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cpu'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21446bbc",
   "metadata": {},
   "source": [
    "## NTK functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6083841e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functions from https://pytorch.org/functorch/stable/notebooks/neural_tangent_kernels.html\n",
    "def fnet_single(params, x):\n",
    "    # unsqueeze returns a new tensor with a dimension of size one inserted at the specified position.\n",
    "    # squeeze removes dimension at specified position.\n",
    "    return fnet(params, x.unsqueeze(0)).squeeze(0)\n",
    "\n",
    "\n",
    "def empirical_ntk_jacobian_contraction(fnet_single, params, x1, x2):\n",
    "    # Compute J(x1)\n",
    "    jac1 = vmap(jacrev(fnet_single), (None, 0))(params, x1)\n",
    "    jac1 = [j.flatten(2) for j in jac1]\n",
    "\n",
    "    # Compute J(x2)\n",
    "    jac2 = vmap(jacrev(fnet_single), (None, 0))(params, x2)\n",
    "    jac2 = [j.flatten(2) for j in jac2]\n",
    "\n",
    "    # Compute J(x1) @ J(x2).T\n",
    "    result = torch.stack([torch.einsum('Naf,Mbf->NMab', j1, j2) for j1, j2 in zip(jac1, jac2)])\n",
    "    result = result.sum(0)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdea6107",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9ab2e36",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FNNLayer(nn.Module):\n",
    "    def __init__(self, input_d, output_d, activation, bias = True):\n",
    "        super(FNNLayer, self).__init__()\n",
    "        if activation == 'relu':\n",
    "            self.activation = nn.ReLU()\n",
    "        elif activation == 'tanh':\n",
    "            self.activation = nn.Tanh()\n",
    "        elif activation == 'linear':\n",
    "            self.activation = nn.Identity()\n",
    "        else:\n",
    "            print(\"Activation not recognized, using ReLU instead\")\n",
    "            self.activation = nn.ReLU()\n",
    "        self.input_d = input_d\n",
    "        self.linear = nn.Linear(input_d, output_d, bias=bias)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.linear(x)\n",
    "        x = self.activation(x)\n",
    "        return x\n",
    "    \n",
    "\n",
    "    \n",
    "class FNN(nn.Module):\n",
    "    def __init__(self, width, input_d, output_d, activation, num_layers, bias = True):\n",
    "        super(FNN, self).__init__()\n",
    "        self.first_layer = FNNLayer(input_d, width, activation, bias=bias)\n",
    "        self.internal_layers = nn.ModuleList([FNNLayer(width, width, activation, bias=bias) for _ in range(num_layers - 2)])\n",
    "        self.output_layer = FNNLayer(width, output_d, activation=\"linear\", bias=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.flatten(x, start_dim=1, end_dim=- 1) \n",
    "        x = self.first_layer(x)\n",
    "        for layer in self.internal_layers:\n",
    "            x = layer(x)         \n",
    "        x = self.output_layer(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1be1d3db",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN(nn.Module):\n",
    "    def __init__(self, n_c, L, act):\n",
    "        super(CNN, self).__init__()\n",
    "        self.d = d # dimension of square image\n",
    "        self.L = L # number of hidden layers\n",
    "        \n",
    "        # Select activation function\n",
    "        if act == 'relu':\n",
    "            self.act = nn.ReLU()\n",
    "        elif act == 'tanh':\n",
    "            self.act = nn.Tanh()\n",
    "        elif act == 'linear':\n",
    "            self.act = nn.Identity()               \n",
    "        else:\n",
    "            print(\"Activation not recognized, using ReLU instead\")\n",
    "            self.act = nn.ReLU()\n",
    "        \n",
    "        # Define layers\n",
    "        self.conv1 = nn.Conv2d(3, n_c, (5, 5))\n",
    "        self.internal_layers = nn.ModuleList([nn.Conv2d(n_c, n_c, (5, 5)) for _ in range(L - 2)])\n",
    "        self.fc = nn.Linear(n_c*(d - 4*(L-1))*(d - 4*(L-1)), 1)\n",
    "            \n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.act(x)\n",
    "        \n",
    "        for layer in self.internal_layers:\n",
    "            x = layer(x)\n",
    "            x = self.act(x)\n",
    "        \n",
    "        x = torch.flatten(x, start_dim=1, end_dim=- 1)        \n",
    "        x = self.fc(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cdcccf5",
   "metadata": {},
   "source": [
    "# Import caltech 101 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "713be162",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = 40 # assume images are square and have d pixels per side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b429ef10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "path = \"../data/caltech101/101_ObjectCategories\"\n",
    "\n",
    "\n",
    "T = transforms.Compose([\n",
    "    transforms.RandomResizedCrop(size=d),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Lambda(lambda x: x.repeat(3, 1, 1) if x.size(0)==1 else x),\n",
    "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "])\n",
    "c101_data = datasets.Caltech101(root=path, transform=T, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ad90e10",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd85d729c30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_ex = (c101_data[2000][0]).permute(2,1,0).numpy()\n",
    "plt.imshow(img_ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e30f9cd",
   "metadata": {},
   "source": [
    "# Generate NTK matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a2f3e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 200\n",
    "n_trials = 10\n",
    "m = 500\n",
    "n_c = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d46f2fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed trial 1\n",
      "Completed trial 2\n",
      "Completed trial 3\n",
      "Completed trial 4\n",
      "Completed trial 5\n",
      "Completed trial 6\n",
      "Completed trial 7\n",
      "Completed trial 8\n",
      "Completed trial 9\n",
      "Completed trial 10\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(columns = [\"tn\", \"ds\", \"type\", \"act\", \"L\", \"evi\", \"ev\"])\n",
    "for t in range(n_trials):\n",
    "    for ds in ['c101', 'gauss']:\n",
    "        if ds == 'c101':\n",
    "           # Generate Caltec101 data for this trial\n",
    "            batch = torch.empty((batch_size, 3, d, d))\n",
    "            ri = rng.choice(len(c101_data), size=batch_size, replace=False)\n",
    "            for j in range(batch_size):\n",
    "                batch[j] = c101_data[ri[j]][0]\n",
    "        else:\n",
    "            # Generate Gaussian data for this trial\n",
    "            batch = torch.randn(batch_size, 3, d, d)\n",
    "               \n",
    "        for act in ['relu', 'tanh', 'linear']:\n",
    "            for L in [2, 5]:\n",
    "                for typ in ['fnn', 'cnn']:\n",
    "                    if typ == 'fnn':\n",
    "                        #width, input_d, output_d, act, num_layers\n",
    "                        model = FNN(m, 3*d*d, 1, act, L)\n",
    "                    else:\n",
    "                        model = CNN(n_c,L,act)   \n",
    "                    fnet, params = make_functional(model)\n",
    "                    temp = empirical_ntk_jacobian_contraction(fnet_single, params, batch, batch).squeeze()\n",
    "                    ntk_matrix = temp.detach().numpy()\n",
    "                    eigs,_= LA.eig(ntk_matrix)\n",
    "                    eigs_sort = np.sort(eigs)[::-1]\n",
    "                    eigs_sort = eigs_sort/eigs_sort[0]\n",
    "                    temp_dict = {\"tn\":[t for i in range(batch_size)],\n",
    "                        \"ds\": [ds for i in range(batch_size)],\n",
    "                        \"type\": [typ for i in range(batch_size)],\n",
    "                        \"act\": [act for i in range(batch_size)],\n",
    "                        \"L\": [L for i in range(batch_size)],\n",
    "                        \"evi\": np.arange(1,batch_size+1),\n",
    "                        \"ev\": eigs_sort\n",
    "                    }\n",
    "                    temp_df = pd.DataFrame(temp_dict)\n",
    "                    df = pd.concat([df, temp_df], ignore_index = True)\n",
    "\n",
    "        # Compute spectrum of data\n",
    "        data_matrix = batch.flatten(1).detach().numpy()\n",
    "        data_gram = data_matrix@data_matrix.T\n",
    "        eigs,_=LA.eig(data_gram)\n",
    "        eigs_sort = np.sort(eigs)[::-1]\n",
    "        eigs_sort = eigs_sort/eigs_sort[0]\n",
    "        \n",
    "        temp_dict = {\"tn\":[t for i in range(batch_size)],\n",
    "            \"ds\": [ds for i in range(batch_size)],\n",
    "            \"type\": ['data' for i in range(batch_size)],\n",
    "            \"act\": ['data' for i in range(batch_size)],\n",
    "            \"L\": [L for i in range(batch_size)],\n",
    "            \"evi\": np.arange(1,batch_size+1),\n",
    "            \"ev\": eigs_sort\n",
    "        }\n",
    "        temp_df = pd.DataFrame(temp_dict)\n",
    "        df = pd.concat([df, temp_df], ignore_index = True)\n",
    "        \n",
    "    print(\"Completed trial \" + str(t+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed26bfdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save dataframe to \n",
    "df.to_csv(\"spectral_data2.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b79c009",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d7767b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_dict = {\"tn\":\"tn\",\n",
    "            \"ds\": \"ds\",\n",
    "            \"type\": \"type\",\n",
    "            \"act\": \"Activation\",\n",
    "            \"L\": \"L\",\n",
    "            \"evi\": \"Eigenvalue Index\",\n",
    "            \"ev\": \"Normalized Magnitude\"}\n",
    "# call rename () method\n",
    "df.rename(columns=name_dict,\n",
    "          inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e2648aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slice up the data\n",
    "df_100 = df[(df['Eigenvalue Index']<101)]\n",
    "\n",
    "# Get data data-frame\n",
    "df_data = df_100[df_100[\"type\"]==\"data\"]\n",
    "df_data_c101 = df_data[df_data[\"ds\"]==\"c101\"]\n",
    "df_data_gauss = df_data[df_data[\"ds\"]==\"gauss\"]\n",
    "\n",
    "# Generate data frames for cnn\n",
    "df_cnn = df_100[df_100[\"type\"]==\"cnn\"]\n",
    "df_cnn_2_c101 = df_cnn[(df_cnn[\"L\"]==2)&(df_cnn[\"ds\"]==\"c101\")]\n",
    "df_cnn_5_c101 =  df_cnn[(df_cnn[\"L\"]==5)&(df_cnn[\"ds\"]==\"c101\")]\n",
    "df_cnn_2_gauss = df_cnn[(df_cnn[\"L\"]==2)&(df_cnn[\"ds\"]==\"gauss\")]\n",
    "df_cnn_5_gauss =  df_cnn[(df_cnn[\"L\"]==5)&(df_cnn[\"ds\"]==\"gauss\")]\n",
    "\n",
    "# Generate data frames for fnn\n",
    "df_fnn = df_100[df_100[\"type\"]==\"fnn\"]\n",
    "df_fnn_2_c101 = df_fnn[(df_fnn[\"L\"]==2)&(df_fnn[\"ds\"]==\"c101\")]\n",
    "df_fnn_5_c101 =  df_fnn[(df_fnn[\"L\"]==5)&(df_fnn[\"ds\"]==\"c101\")]\n",
    "df_fnn_2_gauss = df_fnn[(df_fnn[\"L\"]==2)&(df_fnn[\"ds\"]==\"gauss\")]\n",
    "df_fnn_5_gauss =  df_fnn[(df_fnn[\"L\"]==5)&(df_fnn[\"ds\"]==\"gauss\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb1b7612",
   "metadata": {},
   "source": [
    "### Feedforward neural networks spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7c703d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme()\n",
    "fig1, ax = plt.subplots(2,2, figsize=(12,6), sharex=True, sharey=True)\n",
    "sns.lineplot(data=df_fnn_2_c101, x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\", ax = ax[0,0]).set(title='NTK Spectrum: Caltech101, Depth=2')\n",
    "sns.lineplot(data=df_data_c101,  x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\",palette=['red'], ax = ax[0,0])\n",
    "sns.lineplot(data=df_fnn_5_c101, x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\", ax = ax[1,0]).set(title='NTK Spectrum: Caltech101, Depth=5')\n",
    "sns.lineplot(data=df_data_c101,  x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\",palette=['red'], ax = ax[1,0])\n",
    "sns.lineplot(data=df_fnn_2_gauss, x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\", ax = ax[0,1]).set(title='NTK Spectrum: Gaussian Data, Depth=2')\n",
    "sns.lineplot(data=df_data_gauss,  x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\",palette=['red'], ax = ax[0,1])\n",
    "sns.lineplot(data=df_fnn_5_gauss, x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\", ax = ax[1,1]).set(title='NTK Spectrum: Gaussian Data, Depth=5')\n",
    "sns.lineplot(data=df_data_gauss,  x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\",palette=['red'], ax = ax[1,1])\n",
    "ax[0,0].set_yscale('log')\n",
    "ax[0,1].get_legend().remove()\n",
    "ax[1,0].get_legend().remove()\n",
    "ax[1,1].get_legend().remove()\n",
    "# fig1.suptitle('FFNN Architecture', fontsize=12)\n",
    "sns.set(font_scale=1.15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a10f25c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig1.savefig(\"fnn_spectrums2.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "052f3083",
   "metadata": {},
   "source": [
    "### Convolutional neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e58f0750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme()\n",
    "fig2, ax = plt.subplots(2,2, figsize=(12,6), sharex=True, sharey=True)\n",
    "sns.lineplot(data=df_cnn_2_c101, x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\", ax = ax[0,0]).set(title='NTK Spectrum: Caltech101, Depth=2')\n",
    "sns.lineplot(data=df_data_c101,  x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\",palette=['red'], ax = ax[0,0])\n",
    "sns.lineplot(data=df_cnn_5_c101, x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\", ax = ax[1,0]).set(title='NTK Spectrum: Caltech101, Depth=5')\n",
    "sns.lineplot(data=df_data_c101,  x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\",palette=['red'], ax = ax[1,0])\n",
    "sns.lineplot(data=df_cnn_2_gauss, x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\", ax = ax[0,1]).set(title='NTK Spectrum: Gaussian data, Depth=2')\n",
    "sns.lineplot(data=df_data_gauss,  x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\",palette=['red'], ax = ax[0,1])\n",
    "sns.lineplot(data=df_cnn_5_gauss, x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\", ax = ax[1,1]).set(title='NTK Spectrum: Gaussian data, Depth=5')\n",
    "sns.lineplot(data=df_data_gauss,  x=\"Eigenvalue Index\", y=\"Normalized Magnitude\", hue=\"Activation\",palette=['red'], ax = ax[1,1])\n",
    "ax[0,0].set_yscale('log')\n",
    "ax[0,1].get_legend().remove()\n",
    "ax[1,0].get_legend().remove()\n",
    "ax[1,1].get_legend().remove()\n",
    "# fig2.suptitle('CNN Architecture', fontsize=12)\n",
    "sns.set(font_scale=1.15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "83e8707a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig2.savefig(\"cnn_spectrums2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4f74b5a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ntk_ps",
   "language": "python",
   "name": "ntk_ps"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
